The recognition of human activities based on WiFi Channel State Information
(CSI) enables contactless and visual privacy-preserving sensing in indoor
environments. However, poor model generalization, due to varying environmental
conditions and sensing hardware, is a well-known problem in this space. To
address this issue, in this work, data augmentation techniques commonly used in
image-based learning are applied to WiFi CSI to investigate their effects on
model generalization performance in cross-scenario and cross-system settings.
In particular, we focus on the generalization between line-of-sight (LOS) and
non-line-of-sight (NLOS) through-wall scenarios, as well as on the
generalization between different antenna systems, which remains under-explored.
We collect and make publicly available a dataset of CSI amplitude spectrograms
of human activities. Utilizing this data, an ablation study is conducted in
which activity recognition models based on the EfficientNetV2 architecture are
trained, allowing us to assess the effects of each augmentation on model
generalization performance. The gathered results show that specific
combinations of simple data augmentation techniques applied to CSI amplitude
data can significantly improve cross-scenario and cross-system generalization.

基于 WiFi 信道状态信息（CSI）的人类活动识别实现了室内的非接触和视觉隐私保护感知。然而，由于环境条件和传感硬件的变化导致模型推广能力较差，这是该领域一个众所周知的问题。为了解决这个问题，在本研究中将常用于基于图像学习的数据增强技术应用于 WiFi CSI，探究它们对跨场景和跨系统设置下模型推广能力的影响。特别关注了直视（LOS）和穿墙非直视（NLOS）场景之间以及不同天线系统之间的推广能力，此前研究较少。我们收集并公开了一个人类活动 CSI 振幅谱图的数据集。利用这些数据，进行了一个消融研究，使用 EfficientNetV2 架构训练了基于活动识别模型，从而评估每种数据增强技术对模型推广能力的影响。实验结果显示，应用于 CSI 振幅数据的特定数据增强技术组合可以显著提高跨场景和跨系统的推广能力。